Pulmonology

Sparse Bayesian Learning for Label Efficiency in Cardiac Real-Time MRI

Cardiac real-time magnetic resonance imaging (MRI) is an emerging technology that images the heart at up to 50 frames per second, offering insight into the respiratory effects on the heartbeat. However, this method significantly increases the number of images that must be segmented to derive critical health indicators. Although neural networks perform well on inner slices, predictions on outer s...

Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registries: Performance Across Model Sizes, Versions, and Parameters

Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet our understanding of optimal management remains limited due to heterogeneous and inaccessible radiology documentation. The PERT Consortium registry standardizes PE management data but depends on resource-intensive manual abstraction. Large language models (LLMs) offer a scalable alternative for automating concept extract...

AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. A crucial challenge for early diagnosis is differentiating unce...

Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining

The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and progno...

Unpaired Translation of Chest X-ray Images for Lung Opacity Diagnosis via Adaptive Activation Masks and Cross-Domain Alignment

Chest X-ray radiographs (CXRs) play a pivotal role in diagnosing and monitoring cardiopulmonary diseases. However, lung opacities in CXRs frequently...

PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping

Computational phenotyping is essential for biomedical research but often requires significant time and resources, especially since traditional metho...

Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification

Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and ...

Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis

Computer-aided pathology detection algorithms for video-based imaging modalities must accurately interpret complex spatiotemporal information by int...

Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anato...

Symbolic Audio Classification via Modal Decision Tree Learning

The range of potential applications of acoustic analysis is wide. Classification of sounds, in particular, is a typical machine learning task that r...

High Accuracy Pulmonary Vessel Segmentation for Contrast and Non-contrast CT Images and Clinical Evaluation

Accurate segmentation of pulmonary vessels plays a very critical role in diagnosing and assessing various lung diseases. Currently, many automated a...

Model development and validation for predicting small-cell lung cancer bone metastasis utilizing diverse machine learning algorithms based on the SEER database.

The aim of this study was to devise a machine learning algorithm with superior performance in predicting bone metastasis (BM) in small cell lung cance...

[Application Practice of AI Empowering Post-discharge Specialized Disease Management in Postoperative Rehabilitation of the Lung Cancer Patients Undergoing Surgery].

BACKGROUND: Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widesprea...

Predicting Cardiopulmonary Exercise Testing Outcomes in Congenital Heart Disease Through Multi-modal Data Integration and Geometric Learning

Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables includin...

SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis

Spread through air spaces (STAS) represents a newly identified aggressive pattern in lung cancer, which is known to be associated with adverse progn...

The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances

Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes. This article pr...

Vision-Language Models for Acute Tuberculosis Diagnosis: A Multimodal Approach Combining Imaging and Clinical Data

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) s...

Advancing Chronic Tuberculosis Diagnostics Using Vision-Language Models: A Multi modal Framework for Precision Analysis

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated ch...

Enhancing zero-shot learning in medical imaging: integrating clip with advanced techniques for improved chest x-ray analysis

Due to the large volume of medical imaging data, advanced AI methodologies are needed to assist radiologists in diagnosing thoracic diseases from ch...

DCAT: Dual Cross-Attention Fusion for Disease Classification in Radiological Images with Uncertainty Estimation

Accurate and reliable image classification is crucial in radiology, where diagnostic decisions significantly impact patient outcomes. Conventional d...